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Foolproof Cooperative Learning
June 24, 2019 Β· Declared Dead Β· π Asian Conference on Machine Learning
"No code URL or promise found in abstract"
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Authors
Alexis Jacq, Julien Perolat, Matthieu Geist, Olivier Pietquin
arXiv ID
1906.09831
Category
cs.GT: Game Theory
Cross-listed
cs.AI,
cs.LG
Citations
9
Venue
Asian Conference on Machine Learning
Last Checked
4 months ago
Abstract
This paper extends the notion of learning equilibrium in game theory from matrix games to stochastic games. We introduce Foolproof Cooperative Learning (FCL), an algorithm that converges to a Tit-for-Tat behavior. It allows cooperative strategies when played against itself while being not exploitable by selfish players. We prove that in repeated symmetric games, this algorithm is a learning equilibrium. We illustrate the behavior of FCL on symmetric matrix and grid games, and its robustness to selfish learners.
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